Match Fixing is often times described as the next big fight for the sporting world with Tennis being one of the most endangered sports. While the phenomenon is widely recognized, there is a lack of efficient strategies to fight it, as the underlying processes and mechanisms remain largely unclear. In this context, the technique of agent-based modeling may help identifying incentives for athletes to fix a match and to develop effective countermeasures. Therefore, for this master thesis an agent-based model is to be developed by mapping match fixing behavior through the interaction of different parties. The simulation model is to be programmed using the open-source agent-based modeling simulation toolkit Repast, whereby a special focus should lie on integrating interfaces into the model to specifically consider empirical input parameters such as game statistics or betting odds.